This is semantics. We use Big Data now to refer to AI, machine learning and predictive analytics in one easy term. Watson is much more than just “data.”

I don’t think “Big Data” was ever well defined, and the systems that might actually make use of the insights were not in place in retail operations.

And then there was omnichannel, which overtook corporate IT spending (along with security).

I think the biggest issue with retailers and brands is not that they don’t understand the limitation of data science — it’s more that the data scientists have gone into financial services, where they can make real money. It’s on the vendors to provide the science and make it easily digestible for the retailers, et al.

If you read yesterday’s Wall Street Journal interview with the chairman of Fast Retailing (Uniqlo), you might have seen this perceptive comment: “Data would never substitute the merchant. How do you interpret the data? That’s the merchant’s skill set. You need to uncover the insight that is buried in the data and the merchants need to uncover it. Even if you employ artificial intelligence to help you, the numbers [don’t tell] the future.”

His point is well taken: No matter how much importance a retail organization places on its ability to extract data from its transactions, the information means nothing if it can’t be turned into action — and some of that decision-making rests on instinct and experience.

That being said, declaring that “Big Data is dead” is an overreaction. The phrase itself may be overused, but data science is alive and well in the interest of smarter merchandising decisions, loyalty programs and so forth. Would Amazon be where it is today without groundbreaking use of data to develop its predictive technology? I don’t think so.

Back in 2013 I delivered a paper at ESOMAR’s congress on why Big Data was not such a big deal — this echos a lot of what I said at the time. I’m not sure this was ever a “progressive” movement. We were sold a bill of goods on possibilities that turned out not to exist. I’ve always maintained that marketers and retailers need to focus on the questions they would like answered and let their researchers find the best way to answer them. We shouldn’t put the cart before the horse, trying to leverage the data we have, just to try and leverage it.

Similar to the apprehension retail industry experts have about the term “omnichannel,” Big Data now shares a similar distinction.

Big Data is a term that was intended to simplify the complexity of all of your data sources, consolidate it, and present it back in a form that is both consumable and able to be operationalized.

Essentially we should retire this term, as it overflows into the predictive analytics, augmented intelligence, cognitive data and artificial intelligence arenas, which are far better defined and are undergoing significant evolutions.

I’m with Stephen Needel on this. Retail (and most other organizations) are prone to a “band-wagon” fetish. Too often we jump into things like Big Data out of desperation with little insight or foresight. Too much fertilizer is called weed killer. Too much data is called stagnation and paralysis.

The best way for a retailer to leverage data? Think outside the box of AI, ML, segmentation, visually alike, collaborative filtering and the like. The best data to leverage? Customer preference intelligence. Daily individual customers tell a retailer what they like and do not like. Customer preference microdata, cognitively translated by an expert system able to understand the inter-relationship between thousands of sensory attribute data points in a product to uncover the specific, individual sensory preferences of a human customer; in other words, like a human thinks and processes data.

AI and machine learning are still in their infancy when it comes to processing data like the human brain, lacking the ability to understand the subjective nuances between a person and a product to truly leverage data in personalized recommendations. Retailers have reams of micro customer preference intelligence accessible right before their eyes.

Marketers looking to best leverage the data at their disposal should collaborate across agency, internal IT and data science around their customer journeys or customer experiences. The label “Big Data” doesn’t really matter — it’s all about the analytics maturity of a retailer and how they are organized.

Too many retailers have bolted on data science, marketers don’t speak their language, and the data that agencies manage remains fenced off from transactional and customer interaction data created by internal systems. Speed, speed, speed and test, test, test, while quickly deploying to production or scaling the highest value use cases has got to be a priority.

Advancements in AI are like an octane booster to get retailers moving faster and taking advantage of methods they haven’t yet productionized like relatively straightforward personalization. The leaders are extending the gap here and most retailers have got to catch up quickly.

The idea of “Big Data” stumbled because it was too much data for us humans to make sense of. That ushered in “Small Data;” more useable chunks of data that a data scientist could use to model with.

I think the recent advancements in machine learning now allow us to go back to making sense of “Big Data,” or lots of data. I’m not sure retailers are at the point of needing to understand the limitations of data science, as I think they still have not yet grasped the potential. Marketers should be clear on the business problem they are trying to solve for, then enlist data science to determine the right data to model in order to draw out insights.

Data does NOT think, nor is it creative. It is gas in the tank, not even the vehicle, much less the driver.

Thought provoking Rick, but I’m not sure most companies and brands, and retailers (other than AMZN and WMT especially) have fully embraced leveraging data to even get near a point where they have pushed the limits.

I’d challenge anyone reading this to name three retailers other than AMZN that demonstrate to you as a customer that they know “enough” about you to be truly relevant, improve your customer experience AND make you feel like that retailer is loyalty to YOU.

Most retailers still lack a single view of customers, much less one that is fully informed with the right data, including contextual.

All content will be data-driven. Regardless of the buzzword that’s used to describe or define the workflow, the fact remains that a broad spectrum of data needs to be leveraged to drive efficacy and optimization. It’s the only way digitally empowered shoppers will receive content that they value. It’s also the only way that brands can publish and deliver valued content.

Big data is far from dead — it’s just been renamed “AI” which is primarily using algorithms to process data. And that’s too bad. Because the term AI has led to an entirely new round of outsized hype.

Here’s what I believe we learned:

1. There is value to be learned by analyzing data.
2. It’s exceptionally rare that the value is large — as we know from decades analyzing data in direct response marketing it reveals small tweaks that are helpful but not earth shattering.
3. Big data and AI both require massive investments. And both return smaller incremental value. So we need to be very cautious about those investments.
4. Data/AI is not an end of its own. It merely complements other market intelligence like qualitative and quantitative research.
5. It’s deadly to retail when management lets the data make decisions.

The big lesson from the Data/AI adventure is that effective retail success when people make smart, responsible decisions after reviewing the available research, data, analytics, and human behaviors.

What we choose to call it isn’t what’s important — it’s all in the execution. Being able to apply big data based insights into tangible marketing and operational processes is still elusive for many if not most retailers. Most retailers I talk to will tell you they have more data than they know what to do with — it’s not about collecting and analyzing, it’s about applying the insight to their business. They just don’t have enough people in the right roles with the right experience to execute well. And that goes for marketing, operations, everywhere. There are small gains being made, but largely I believe there is much room for improvement. More data scientists would help, but as others here have pointed out, there’s more money in financial services for those folks than there is (so far) in retail.